

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class        A__(a_							):
						"""simple docstring"""






						_A    :				Optional[Any]       					=			['''image_processor''', '''tokenizer''']
						_A    :				Any       					=			'''LayoutLMv3ImageProcessor'''
						_A    :				str       					=			('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')





						def __init__(    self  ,      _lowercase=None  ,      _lowercase=None  ,      **_lowercase    )     -> List[str]:
									a_						:						List[str] 							= None
									if "feature_extractor" in kwargs:
												warnings.warn(
												    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
												    """ instead."""  ,      _lowercase  ,      )
												a_						:						Dict 							= kwargs.pop("""feature_extractor"""    )

									a_						:						Any 							= image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
												raise ValueError("""You need to specify an `image_processor`."""    )
									if tokenizer is None:
												raise ValueError("""You need to specify a `tokenizer`."""    )

									super().__init__(_lowercase  ,      _lowercase    )





						def __call__(    self  ,      _lowercase  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = True  ,      _lowercase = False  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = 0  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = False  ,      _lowercase = False  ,      _lowercase = False  ,      _lowercase = False  ,      _lowercase = True  ,      _lowercase = None  ,      **_lowercase  ,      )     -> BatchEncoding:
									# verify input
									if self.image_processor.apply_ocr and (boxes is not None):
												raise ValueError(
												    """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""    )

									if self.image_processor.apply_ocr and (word_labels is not None):
												raise ValueError(
												    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""    )

									# first, apply the image processor
									a_						:						Optional[Any] 							= self.image_processor(images=_lowercase  ,      return_tensors=_lowercase    )

									# second, apply the tokenizer
									if text is not None and self.image_processor.apply_ocr and text_pair is None:
												if isinstance(_lowercase  ,      _lowercase    ):
															a_						:						List[str] 							= [text]  # add batch dimension (as the image processor always adds a batch dimension)
												a_						:						List[Any] 							= features["""words"""]

									a_						:						Dict 							= self.tokenizer(
									    text=text if text is not None else features["""words"""]  ,      text_pair=text_pair if text_pair is not None else None  ,      boxes=boxes if boxes is not None else features["""boxes"""]  ,      word_labels=_lowercase  ,      add_special_tokens=_lowercase  ,      padding=_lowercase  ,      truncation=_lowercase  ,      max_length=_lowercase  ,      stride=_lowercase  ,      pad_to_multiple_of=_lowercase  ,      return_token_type_ids=_lowercase  ,      return_attention_mask=_lowercase  ,      return_overflowing_tokens=_lowercase  ,      return_special_tokens_mask=_lowercase  ,      return_offsets_mapping=_lowercase  ,      return_length=_lowercase  ,      verbose=_lowercase  ,      return_tensors=_lowercase  ,      **_lowercase  ,      )

									# add pixel values
									a_						:						List[str] 							= features.pop("""pixel_values"""    )
									if return_overflowing_tokens is True:
												a_						:						Union[str, Any] 							= self.get_overflowing_images(_lowercase  ,      encoded_inputs["""overflow_to_sample_mapping"""]    )
									a_						:						Any 							= images

									return encoded_inputs





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase    )     -> Any:
									# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
									a_						:						Optional[int] 							= []
									for sample_idx in overflow_to_sample_mapping:
												images_with_overflow.append(images[sample_idx]    )

									if len(_lowercase    ) != len(_lowercase    ):
												raise ValueError(
												    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
												    F''' {len(_lowercase    )} and {len(_lowercase    )}'''    )

									return images_with_overflow





						def 			UpperCamelCase__					(    self  ,      *_lowercase  ,      **_lowercase    )     -> Union[str, Any]:
									return self.tokenizer.batch_decode(*_lowercase  ,      **_lowercase    )





						def 			UpperCamelCase__					(    self  ,      *_lowercase  ,      **_lowercase    )     -> str:
									return self.tokenizer.decode(*_lowercase  ,      **_lowercase    )





						@property
						def 			UpperCamelCase__					(    self    )     -> str:
									return ["input_ids", "bbox", "attention_mask", "pixel_values"]





						@property
						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,      _lowercase  ,      )
									return self.image_processor_class





						@property
						def 			UpperCamelCase__					(    self    )     -> Tuple:
									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,      _lowercase  ,      )
									return self.image_processor





import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__snake_case				:      Union[str, Any]   		=			logging.get_logger(__name__)

__snake_case				:      Tuple   		=			OrderedDict(
    [
        ("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
        ("""beit""", """BeitFeatureExtractor"""),
        ("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
        ("""clap""", """ClapFeatureExtractor"""),
        ("""clip""", """CLIPFeatureExtractor"""),
        ("""clipseg""", """ViTFeatureExtractor"""),
        ("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
        ("""convnext""", """ConvNextFeatureExtractor"""),
        ("""cvt""", """ConvNextFeatureExtractor"""),
        ("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
        ("""data2vec-vision""", """BeitFeatureExtractor"""),
        ("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
        ("""deit""", """DeiTFeatureExtractor"""),
        ("""detr""", """DetrFeatureExtractor"""),
        ("""dinat""", """ViTFeatureExtractor"""),
        ("""donut-swin""", """DonutFeatureExtractor"""),
        ("""dpt""", """DPTFeatureExtractor"""),
        ("""encodec""", """EncodecFeatureExtractor"""),
        ("""flava""", """FlavaFeatureExtractor"""),
        ("""glpn""", """GLPNFeatureExtractor"""),
        ("""groupvit""", """CLIPFeatureExtractor"""),
        ("""hubert""", """Wav2Vec2FeatureExtractor"""),
        ("""imagegpt""", """ImageGPTFeatureExtractor"""),
        ("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
        ("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
        ("""levit""", """LevitFeatureExtractor"""),
        ("""maskformer""", """MaskFormerFeatureExtractor"""),
        ("""mctct""", """MCTCTFeatureExtractor"""),
        ("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
        ("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
        ("""mobilevit""", """MobileViTFeatureExtractor"""),
        ("""nat""", """ViTFeatureExtractor"""),
        ("""owlvit""", """OwlViTFeatureExtractor"""),
        ("""perceiver""", """PerceiverFeatureExtractor"""),
        ("""poolformer""", """PoolFormerFeatureExtractor"""),
        ("""regnet""", """ConvNextFeatureExtractor"""),
        ("""resnet""", """ConvNextFeatureExtractor"""),
        ("""segformer""", """SegformerFeatureExtractor"""),
        ("""sew""", """Wav2Vec2FeatureExtractor"""),
        ("""sew-d""", """Wav2Vec2FeatureExtractor"""),
        ("""speech_to_text""", """Speech2TextFeatureExtractor"""),
        ("""speecht5""", """SpeechT5FeatureExtractor"""),
        ("""swiftformer""", """ViTFeatureExtractor"""),
        ("""swin""", """ViTFeatureExtractor"""),
        ("""swinv2""", """ViTFeatureExtractor"""),
        ("""table-transformer""", """DetrFeatureExtractor"""),
        ("""timesformer""", """VideoMAEFeatureExtractor"""),
        ("""tvlt""", """TvltFeatureExtractor"""),
        ("""unispeech""", """Wav2Vec2FeatureExtractor"""),
        ("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
        ("""van""", """ConvNextFeatureExtractor"""),
        ("""videomae""", """VideoMAEFeatureExtractor"""),
        ("""vilt""", """ViltFeatureExtractor"""),
        ("""vit""", """ViTFeatureExtractor"""),
        ("""vit_mae""", """ViTFeatureExtractor"""),
        ("""vit_msn""", """ViTFeatureExtractor"""),
        ("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
        ("""wavlm""", """Wav2Vec2FeatureExtractor"""),
        ("""whisper""", """WhisperFeatureExtractor"""),
        ("""xclip""", """CLIPFeatureExtractor"""),
        ("""yolos""", """YolosFeatureExtractor"""),
    ]
)

__snake_case				:      List[Any]   		=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
						if class_name in extractors:
									a_						:						Tuple 							= model_type_to_module_name(a__)

									a_						:						Any 							= importlib.import_module(f'''.{module_name}'''  ,	"""transformers.models""")
									try:
												return getattr(a__  ,	a__)
									except AttributeError:
												continue

			for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
						if getattr(a__  ,	"""__name__"""  ,	a__) == class_name:
									return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
			a_						:						Tuple 							= importlib.import_module("""transformers""")
			if hasattr(a__  ,	a__):
						return getattr(a__  ,	a__)

			return None


def 					_UpperCAmelCase				(						a__  ,	a__ = None  ,	a__ = False  ,	a__ = False  ,	a__ = None  ,	a__ = None  ,	a__ = None  ,	a__ = False  ,	**a__  ,	):

			'''simple docstring'''



			a_						:						List[str] 							= get_file_from_repo(
			    a__  ,	a__  ,	cache_dir=a__  ,	force_download=a__  ,	resume_download=a__  ,	proxies=a__  ,	use_auth_token=a__  ,	revision=a__  ,	local_files_only=a__  ,	)
			if resolved_config_file is None:
						logger.info(
						    """Could not locate the feature extractor configuration file, will try to use the model config instead.""")
						return {}

			with open(a__  ,	encoding="""utf-8""") as reader:
						return json.load(a__)



class        A__:
						"""simple docstring"""






						def __init__(    self    )     -> List[str]:
									raise EnvironmentError(
									    """AutoFeatureExtractor is designed to be instantiated """
									    """using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."""    )





						@classmethod
						@replace_list_option_in_docstrings(_lowercase    )
						def 			UpperCamelCase__					(    cls  ,      _lowercase  ,      **_lowercase    )     -> Dict:
									a_						:						Tuple 							= kwargs.pop("""config"""  ,      _lowercase    )
									a_						:						Dict 							= kwargs.pop("""trust_remote_code"""  ,      _lowercase    )
									a_						:						Dict 							= True

									a_							,					a_						:						List[str] 							= FeatureExtractionMixin.get_feature_extractor_dict(_lowercase  ,      **_lowercase    )
									a_						:						Tuple 							= config_dict.get("""feature_extractor_type"""  ,      _lowercase    )
									a_						:						List[Any] 							= None
									if "AutoFeatureExtractor" in config_dict.get("""auto_map"""  ,      {}    ):
												a_						:						Any 							= config_dict["""auto_map"""]["""AutoFeatureExtractor"""]

									# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
									if feature_extractor_class is None and feature_extractor_auto_map is None:
												if not isinstance(_lowercase  ,      _lowercase    ):
															a_						:						List[str] 							= AutoConfig.from_pretrained(_lowercase  ,      **_lowercase    )
												# It could be in `config.feature_extractor_type``
												a_						:						List[Any] 							= getattr(_lowercase  ,      """feature_extractor_type"""  ,      _lowercase    )
												if hasattr(_lowercase  ,      """auto_map"""    ) and "AutoFeatureExtractor" in config.auto_map:
															a_						:						List[str] 							= config.auto_map["""AutoFeatureExtractor"""]

									if feature_extractor_class is not None:
												a_						:						int 							= feature_extractor_class_from_name(_lowercase    )

									a_						:						Dict 							= feature_extractor_auto_map is not None
									a_						:						Union[str, Any] 							= feature_extractor_class is not None or type(_lowercase    ) in FEATURE_EXTRACTOR_MAPPING
									a_						:						Optional[Any] 							= resolve_trust_remote_code(
									    _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase    )

									if has_remote_code and trust_remote_code:
												a_						:						int 							= get_class_from_dynamic_module(
												    _lowercase  ,      _lowercase  ,      **_lowercase    )
												a_						:						Tuple 							= kwargs.pop("""code_revision"""  ,      _lowercase    )
												if os.path.isdir(_lowercase    ):
															feature_extractor_class.register_for_auto_class()
												return feature_extractor_class.from_dict(_lowercase  ,      **_lowercase    )
									elif feature_extractor_class is not None:
												return feature_extractor_class.from_dict(_lowercase  ,      **_lowercase    )
									# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
									elif type(_lowercase    ) in FEATURE_EXTRACTOR_MAPPING:
												a_						:						List[str] 							= FEATURE_EXTRACTOR_MAPPING[type(_lowercase    )]
												return feature_extractor_class.from_dict(_lowercase  ,      **_lowercase    )

									raise ValueError(
									    F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
									    F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
									    F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()    )}'''    )





						@staticmethod
						def 			UpperCamelCase__					(    _lowercase  ,      _lowercase    )     -> List[Any]:
									FEATURE_EXTRACTOR_MAPPING.register(_lowercase  ,      _lowercase    )


